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Maximizing minority accuracy for imbalanced pattern classification problems using cost-sensitive Localized Generalization Error Model
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DOI：10.1016/j.asoc.2021.107178.png)
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Traditional machine learning methods may not yield satisfactory generalization capability when samples in different classes are imbalanced. These methods tend to sacrifice the accuracy of the minority class to improve the overall accuracy without regarding the fact that misclassifications of minority samples usually costs more in many real world applications. Therefore, we propose a neural network training method via a minimization of the cost-sensitive localized generalization error-based objective function (c-LGEM) to achieve a better balance of error yielded by the minority and the majority classes. The c-LGEM emphasizes the minimization of the generalization error of the minority class in a cost-sensitive manner. Experimental results obtained on 16 UCI datasets show that neural networks trained by the c-LGEM yield better performance in comparison to the performance yielded by some existing methods. (C) 2021 Elsevier B.V. All rights reserved.
Keyword:
Localized generalization error model (L-GEM)
Cost-sensitive
Multilayer perceptron neural network (MLPNN)
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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